> Markdown version of [/jobs/ext/3863202-principal-machine-learning-engineer-recommendation-platform-feature-platform](https://www.wearedevelopers.com/jobs/ext/3863202-principal-machine-learning-engineer-recommendation-platform-feature-platform). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Machine Learning Engineer, Recommendation Platform (Feature Platform) - **Company:** BLBS, LLC - **Location:** Seattle, WA, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Training Data, Java (Programming Language), Artificial Intelligence, Encodings, Data Synchronization, Machine Learning, Performance Tuning, Recommender Systems, Feature Store, Data Processing, Real Time Systems, Large Language Models, Generative AI, Agentic-AI, Stream Processing - **Published:** October 22, 2026 - **Apply:** https://www.nettemps.com/us/en/W53F81F3A3B2B842BEE.ntap ## About the Role * At least 4 years of recommender systems engineering experience at a consumer Internet company. You have played a significant role in, or led, the launch or architectural modernization of a Real Time recommendation system serving tens of millions of users. Experience building a recommendation system or feature platform from the ground up is preferred. * Strong computer science fundamentals and proficiency in at least one of Go, Java, or C+. You write well-structured code, apply sound design principles, and enjoy tackling challenging engineering problems. * Experience with stream-and-batch data processing and feature and training data pipelines, including hands-on experience resolving stream-processing latency and data backlog issues. Familiarity with the deployment and performance tuning of mainstream big-data systems, key-value stores, and vector databases. * A platform-oriented approach to architecture and standardization. Experience developing core modules for a large-scale recommendation platform, feature platform, experimentation platform, or high-performance RPC framework is preferred. * A deep understanding of the recommendation system lifecycle, with the ability to collaborate effectively with algorithm teams and translate model requirements into reliable production systems. Nice-to-have: * Architecture experience in search, advertising, or recommendations at major technology companies * Experience as a key engineer building cross-business feature platforms, large-scale online and offline feature services, or embedding feature pipelines. * Experience with multi-region deployment, data synchronization, active-active architectures, or cross-region RPC latency optimization. * An AI-driven approach and hands-on experience bringing LLM or agent applications into production for search, advertising, or recommendation use cases. ## Description As a Principal Recommendation Platform Engineer, you will help upgrade our core recommendation architecture and feature platform, evolving our current rule-based personalization system into an industrial-grade, large-scale, Real Time machine learning recommendation platform. The role spans key business scenarios, including trading products, content feeds, the community, and AI-powered investment workflows such as TradeGPT. You will work closely with recommendation algorithm, growth product and operations, and data science teams to build a high-throughput, low-latency, highly available foundation for personalization at scale, serving more than 80 million users globally. You will lead architectural decisions, infrastructure development, and end-to-end delivery, taking the platform from zero to one in a resource-constrained, fast-moving environment., * Develop high-throughput, low-latency recommendation services. Continuously improve system performance and reliability, and establish robust degradation and disaster-recovery mechanisms. * Build stream-and-batch feature computation and training data pipelines to support minute-level updates of user behavior features. Develop a Feature Store that unifies online and offline features, address temporal feature leakage and feature consistency issues, and support faster model iteration and performance improvements. * Identify common architectural needs across recommendation scenarios-including trading products, content feeds, and community-and optimize their workflows. Build reusable platform capabilities to address technical challenges and business requirements. * Develop and optimize core recommendation components, and drive the engineering and business adoption of emerging technologies, including LLMs and AI agents., * A multi-domain recommendation challenge in fintech: Go beyond a single domain such as video or e-commerce. You will build personalization capabilities across trading products, market intelligence, community content, campaigns, and AI-powered investment workflows. * Proven business impact with substantial room for ML advancement: Our current recommendation system has demonstrated a 71% increase in CTR and a 1,285% increase in conversion rate, with significant potential for an ML-native upgrade. * Work at the intersection of AI and recommendations: Build where traditional recommender systems, generative AI, and LLM agents meet-one of the most active frontiers in engineering. * An opportunity to lead, not just follow: Our combination of trading, content, community, AI, and campaigns creates distinctive technical challenges and enables rapid iteration. * Global scale: Serve more than 80 million users across over 200 countries, tackling the real-world complexity of large-scale, Real Time systems.